日本地球惑星科学連合2015年大会

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インターナショナルセッション(ポスター発表)

セッション記号 H (地球人間圏科学) » H-TT 計測技術・研究手法

[H-TT09] GIS

2015年5月25日(月) 18:15 〜 19:30 コンベンションホール (2F)

コンビーナ:*小口 高(東京大学空間情報科学研究センター)、村山 祐司(筑波大学大学院生命環境科学研究科地球環境科学専攻)、柴崎 亮介(東京大学空間情報科学研究センター)、吉川 眞(大阪工業大学工学部)

18:15 〜 19:30

[HTT09-P01] 降雨強度データを用いた機械学習による簡易水害リスク評価手法の構築

*佐藤 李菜1小口 高2 (1.東京大学大学院新領域創成科学研究科、2.東京大学空間情報科学研究センター)

キーワード:水害, 降雨強度, 機械学習, GIS

Floods especially pluvial flooding, is a major disaster in Japanese urban areas. Simulations are often used to assess flood risk, but such approaches tend to be highly complicated. Therefore, some simple methods using topographical indices, land use and rainfall data with statistical approach have been proposed. However, the accuracy of such methods is still low. This study aims to analyze the characteristics of flooded areas in the 23 wards of Tokyo and construct a simple method for evaluating flood risk using rainfall intensity data and machine learning. Radar rainfall data from the Japan Meteorological Agency were analyzed using Random Forest, a method of machine learning. The accuracy of the models constructed by Random Forest for flooded areas is almost 100% in many districts, but the accuracy for non-flooded areas are low. It means that the models can well predict flood occurrence, but many non-flooded areas are estimated as flooded areas. Therefore, it is necessary to improve the model.